Faye Gillespie, | May 26, 2021
About the Client
Scoot is one of the most high-growth and successful low-cost airlines in the Southern Asian region. As a value-oriented airline it focuses on innovation all along the way of the customer journey. Its aim is to provide an affordable service with great user experience for its clients. Nevertheless, in order to be able to grow and open up new destinations, Scoot needs to understand the profitability of their flights and the real challenges in their operation. In order to successfully achieve the above mentioned goals, it is very important for the company to have a unified data warehouse where they can get a clear view on their operations, flights and customers, learn from the recorded data, and make the necessary decisions to increase the customer experience while keeping the company financially successful as well.
Challenge: scattered data sources
Before starting to build up a data warehouse in Google Cloud Platform, there were several scattered, separated data sources which were not linked together and caused several problems for Scoot:
Solution: Single, consistent, trustful picture
ALiZ developed a GCP based data warehouse in 1 year to Scoot which can provide a single, consistent, trustful picture following the below steps:
By providing an integrated source of data from connecting the most important systems, ALiZ helped Scoot to achieve its goal, and collect and combine data all along the way of the customer journey and able to differentiate profitability on different flights, segments and legs level. But the real business value comes from the insights and personalized interactions themselves that are built on the recorded data. An example use case is understanding the marketing channel effectiveness. With the Google BigQuery based data warehouse housing the necessary data sources together, Scoot is able to stitch each channel’s conversion at the booking and passenger level, unveiling insights that can help them better understand each channel’s effectiveness, optimise marketing spend and select more relevant routes in marketing efforts. Apart from complex reporting, machine learning use cases can be built upon the new system like predicting distressed routes. As the data is housed in EDW, this data feed can be automated in remarketing efforts, thus further optimising marketing spend.
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